Semantic Search
Definition
A search approach that comprehensively understands the meaning and context of queries, improving results beyond simple keyword matching for users.
Use Cases
- Microsoft: Enterprise knowledge search across internal documents (policies, HR content, engineering docs) where employees ask natural-language questions and expect relevant passages, not just keyword matches. — Microsoft 365 Copilot uses retrieval-augmented generation (RAG) patterns that rely on semantic retrieval over Microsoft Graph content. In practice, this involves embedding-based retrieval and relevance ranking over organizational data sources, then grounding responses with retrieved passages. (Improves findability of internal knowledge and reduces time spent searching across multiple repositories by returning contextually relevant content for natural-language queries.)
- Google: E-commerce product discovery where shoppers search with descriptive phrases (e.g., “comfortable running shoes for flat feet”) and expect relevant products even if listings don’t contain the exact words. — Google Cloud’s Vertex AI Search is used by organizations to build semantic search experiences over product catalogs by indexing structured product data and unstructured descriptions, then applying semantic ranking and/or vector embeddings to match intent. (Better product relevance for long, descriptive queries, which can improve engagement and conversion by helping users find suitable items faster.)
- Amazon: Customer support and internal helpdesk search where agents need the best answer from FAQs, manuals, and tickets using natural-language questions. — Amazon Kendra is commonly used to index multiple content sources (e.g., S3, SharePoint, Confluence) and apply ML-based relevance to return the most relevant passages and documents for a question-like query. (Faster issue resolution and improved self-service by surfacing the right knowledge article or passage without requiring exact keyword matches.)
Provider Equivalents
- AWS: Amazon Kendra
- Azure: Azure AI Search
- GCP: Vertex AI Search
- OCI: OCI Search with OpenSearch
Frequently Asked Questions
- What’s the difference between semantic search and keyword search?
- Keyword search matches exact words (or close variants) in documents. Semantic search tries to match meaning and intent. For example, a keyword search might miss results that use different wording, while semantic search can still find them because it understands that related phrases can mean the same thing.
- When should I use semantic search?
- Use it when users ask natural-language questions, use varied wording, or search across messy/unstructured content (PDFs, tickets, chat logs, knowledge bases). It’s especially useful for enterprise knowledge search, customer support portals, and product discovery where synonyms and context matter.
- How much does semantic search cost?
- Cost depends on (1) indexing/storage size, (2) number of queries, (3) whether you generate embeddings (model inference cost), and (4) the infrastructure or managed service tier. Managed services typically charge for search units/replicas and storage, while vector databases add costs for vector indexing and compute. If you use an LLM for RAG, add LLM token costs on top of retrieval.
Category: ai-ml
Difficulty: intermediate
Related Terms
See Also